Foggy or hazy atmospheric conditions present a challenge for video surveillance and other imaging applications, negatively impacting both clarity and contrast. Scattering and attenuation by particles suspended in air interfere with human vision and generally reduce the effectiveness of optical imaging systems, including video surveillance systems. Under hazy conditions, which may result from the presence of haze, mist, fog, smoke, dust, snow, or rain, the light reflected from the subject's surfaces and the ambient light in the medium, known as “airlight”, are absorbed and scattered by the particles in the medium before they reach the camera. The imaging system records the airlight co-located on the image with an attenuated image of the objects of interest in the scene. The amount of attenuation and the amount of airlight depend on the distance between the camera and the object of interest and on the detailed properties of the particles in the atmosphere. The image formed also depends on the ambient illumination at the object or objects of interest and the illumination throughout the volume between the camera and the objects of interest. The size distribution of the particles, the nature of the particles, and the scattering and absorption cross sections of the particles all influence the transfer of radiation in a hazy medium.
In most cases, the particulate parameters creating the haze are not known in sufficient detail to create a complete radiative transfer solution to the imaging situation. In addition, for most surveillance camera installations, an a-priori mapping of distance from the camera to the objects of interest is not available. These considerations have led to haze correction algorithms based on the physics of radiation transfer, but without a detailed physical model. The typical haze correction algorithm thus has a number of parameters that are empirically calibrated and vetted by numerous trials on a variety of outdoor images. In order to efficiently correct images for haze, the correction algorithm must detect haze reliably and differentiate the effect of haze from the a-priori unknown natural haze-free appearance of the scene.
Haze detection allows a regional correction of the image thus improving the appearance of the image, especially when the image contains regions at different distances from the camera. A number of approaches have been disclosed in the prior art for attempting to address the problems associated with obscuring atmospheric conditions. For example, U.S. Pat. No. 6,288,974 of Nelson describes a method in which an image of a reference object is stored in an image processor. One or more detectors sense or detect the reference signal, which is converted into a transformed reference signal, and further sense and transform object signals emanating from an object of interest into a transformed object signal. An image processor generates a corrected image signal by applying an inverse image transfer function to the transformed object signal. R. Fattal (“Single Image Dehazing”, SIGGRAPH, ACM Transactions on Graphics, 2008) describes a method of inferring haze from a pixel by pixel analysis of the image assuming that the effect of haze and the underlying image structure are locally uncorrelated. Mahiny and Turner (“A Comparison of Four Common Atmospheric Correction Methods”, Photogrammetric Engineering & Remote Sensing, Vol. 73, No. 4, Apr. 2007, pp. 361-368) use haze-free reference images taken over multiple dates to provide atmospheric correction to satellite images. Pseudo-invariant features such as rock outcrops, bare soils and built-up areas are segmented and used to normalize images for atmospheric and illumination effects. Narasimhan and Nayar (“Contrast Restoration of Weather Degraded Images”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, No. 6, Jun. 2003, pp. 713-724) propose a physics-based model that describes the appearances of scenes in uniform bad weather conditions. Changes in intensities of scene points under different weather conditions provide constraints to detect depth discontinuities in the scene and also to compute the scene structure.
A number of techniques exist for correcting single images for haze and for correcting video by treating the video as a sequence of independent images. For example He, Sun, and Tang, (“Single Image Haze Removal Using Dark Channel Prior”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 33, No. 12, Dec. 2011, pp. 2341-2353) use the darkest pixel, in any color channel, in small regions to estimate the airlight. They show that this is an effective strategy for many outdoor images. This technique requires a relatively dark pixel in each region of the segmented image in at least one color. For surveillance cameras, the scenes often include regions that contain no particularly dark pixels, for example: nearby building walls, concrete roads, etc. The single image “dark channel prior” type methods tend to overcorrect video when the intrinsic darkness of pixels is overestimated and the artifacts produced by the overcorrection can be distracting to personnel monitoring the video.
Accordingly, the need remains for a method of haze detection to facilitate regional correction of the image to improve the overall appearance of the image, regardless of the distances between the imaged objects and the camera.